Oxidative stress in alcoholic liver disease, focusing on proteins, nucleic acids, and lipids: A review.

Oxidative stress is one of the important factors in the development of alcoholic liver disease. The production of reactive oxygen species and other free radicals is an important feature of alcohol metabolism in the liver and an important substance in liver injury. When large amounts of ROS are produced, the homeostasis of the liver REDOX system will be disrupted and liver injury will be caused. Oxidative stress can damage proteins, nucleic acids and lipids, liver dysfunction. In addition, damaging factors produced by oxidative damage to liver tissue can induce the occurrence of inflammation, thereby aggravating the development of ALD. This article reviews the oxidative damage of alcohol on liver proteins, nucleic acids, and lipids, and provides new insights and summaries of the oxidative stress process. We also discussed the relationship between oxidative stress and inflammation in alcoholic liver disease from different perspectives. Finally, the research status of antioxidant therapy in alcoholic liver disease was summarized, hoping to provide better help for learning and developing the understanding of alcoholic liver disease.

Exploring the Analgesic Initiation Mechanism of Tuina in the Dorsal Root Ganglion of Minor CCI Rats via the TRPV1/TRPA1-cGMP Pathway.

Tuina is a treatment method in traditional Chinese medicine which has analgesic effects and effectively alleviates the symptoms of neuropathic pain (NP). Transient receptor potential vanilloid type 1 (TRPV1) and transient receptor potential ankyrin type 1 (TRPA1) play major roles in transmitting nociceptive sensory signals in the nociceptive primary sensory dorsal root ganglion (DRG) nerve. The nitric oxide (NO)/cyclic guanosine 3',5'-monophosphate(cGMP) pathway exerts both nociceptive and antinociceptive effects in various chronic pain models. TRPV1 and TRPA1 mediate the influx of calcium, which stimulates the generation of NO. Subsequently, NO activates the NO/cGMP/protein kinase G (PKG) signaling pathway, thereby improving hyperalgesia. In the present study, oa rat model of NP with minor chronic constriction injury (CCI) of the right sciatic nerve of NP was established. The results of behavioral testing showed that, after a one-time tuina intervention, the mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) were prolonged to varying degrees in the tuina group compared with the model group. Similarly, the expression of TRPV1, TRPA1, NO, soluble guanylate cyclase ß (sGCß), cGMP, and PKG1 was significantly decreased in the DRG of the tuina and tuina + TRPV1/TRPA1 antagonist group was significantly decreased. These findings suggest that the tuina intervention can effectively improve the symptoms of thermal and mechanical allodynia caused by peripheral nerve injuries. Tuina exerts immediate analgesic effects through the TRPV1/TRPA1-NO-cGMP-PKG signaling pathway.

Metal implant segmentation in CT images based on diffusion model.

BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Blood MALT1 reflects acute exacerbation risk and inflammation in elderly chronic obstructive pulmonary disease patients.

Aim: This study intended to investigate the ability of blood MALT1 to estimate acute exacerbation risk in elderly chronic obstructive pulmonary disease (COPD) patients. Methods: Blood MALT1 was detected in 176 elderly COPD patients (aged more than 60 years). Results: MALT1 was elevated in patients with COPD acute exacerbation versus patients with stable COPD (p < 0.001). In patients with COPD acute exacerbation, MALT1 was negatively related to forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) (p = 0.024) and FEV1% predicted (p = 0.002), but positively linked with global initiative for chronic obstructive lung disease stage (p = 0.005). Conclusion: Blood MALT1 reflects increased acute exacerbation risk and inflammation in elderly COPD patients.

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CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

Background: The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. Methods: We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Results: Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. Conclusions: The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Ecological risks of high-ammonia environment with inhibited growth of Daphnia magna: Disturbed energy metabolism and oxidative stress.

High ammonia pollution is a common problem in water bodies. However, research on the mechanisms underlying the toxic effects on organisms at different nutritional levels is still insufficient. Herein, based on the environmental concentration, the toxic effects of high ammonia pollution on Daphnia magna were investigated. Overall, the feeding and filtration rates of D. magna were significantly decreased by ammonia. Growth inhibition of D. magna by ammonia was confirmed by the decreased body length. After ammonia exposure, the metabolic status of D. magna changed, the correlation network weakened, and the correlations between metabolites were disrupted. Changes occurred in metabolites primarily involved in oxidative stress, fatty acid oxidation, tricarboxylic acid cycle, and protein digestion, absorption, and synthesis, which were validated through alterations in multiple biomarkers. In addition, mitochondrial function was evaluated and was found to inhibit mitochondrial activity, which was accompanied by a decreased marker of mitochondrial activity contents and ATPase activity. Thus, the results suggested that energy metabolism and oxidative stress were involved in ammonia-induced growth toxicity. This study provides new insights into the impact of ammonia on aquatic ecological health.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy.

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Preparation of recombinant neuritin protein.

Neuritin plays an important role in promoting nerve injury repair and maintaining synaptic plasticity, making it a potential therapeutic target for the treatment of nerve injury and neurodegenerative diseases. The present study aimed to obtain an active, unlabeled neuritin protein. Initially, a neuritin protein expression system with an enterokinase site was constructed in Escherichia coli. After optimizing induction conditions and screening for high expression, a neuritin recombinant protein with purity exceeding 85 % was obtained through Ni-affinity chromatography. Subsequently, unlabeled neuritin with a molecular weight of 11 kDa was obtained through the enzymatic cleavage of the His label using an enterokinase. Furthermore, a neuritin recombinant protein with purity exceeding 95 % was obtained using gel chromatography. Functional investigations revealed that neurite outgrowth of PC12 cells was stimulated by the isolated neuritin. This study establishes a method to obtain active and unlabeled neuritin protein, providing a foundation for subsequent research on its biological functions.

Effect of maternal cigarette smoke exposure on COPD progression in offspring mice.

OBJECTIVE: To investigate the impact of maternal smoking on chronic obstructive pulmonary disease (COPD) progression in offspring. METHODS: Using female C57BL/6 J mice, a maternal cigarette smoke exposure (CSE) model was established. Mice were exposed to cigarette smoke for 2 hours/day, 7 days/week, with a minimum 4-hour interval between exposures. Experimental groups included control (Con), pregnancy exposure (AS), pre-pregnancy exposure (SA), and pre-pregnancy + pregnancy exposure (SS). Lung function tests (Penh, PAU, TVb, EF50, Tr) were conducted on male offspring at 7 weeks. Histopathology, electron microscopy, and protein level changes were examined. RESULTS: Lung function tests revealed significant impairments in Penh, PAU, TVb, EF50, and Tr in offspring across all exposure scenarios. Specifically, AS experienced significant lung function impairment and mitochondrial dysfunction in offspring, with noticeable pulmonary lesions and increased apoptosis. SA showed similar or even more severe lung function impairment and cellular apoptosis. SS exhibited the most pronounced effects, with the highest levels of lung dysfunction, mitochondrial damage, and apoptosis. Histopathological analysis showed pulmonary lesions in offspring exposed to maternal CSE. Flow cytometry revealed increased apoptosis and reduced mitochondrial membrane potential in offspring lung cells. Electron microscopy confirmed mitochondrial dysfunction. Upregulation of apoptotic proteins and downregulation of anti-apoptotic protein Bcl-2 were found in offspring lung tissue exposed to maternal CSE. CONCLUSION: Maternal smoking induces impaired lung function, pulmonary lesions, and mitochondrial dysfunction in offspring, regardless of exposure timing and duration. Additionally, it alters expression of apoptosis-related proteins in offspring lung tissue, potentially contributing to COPD susceptibility.

Effects of Maternal Smoke Exposure on Endoplasmic Reticulum Stress and Autophagy in the Lungs of Offspring Mice.

BACKGROUND: Cigarette smoke (CS) induces autophagy and endoplasmic reticulum (ER) stress in the lungs. Research suggests that maternal exposure to CS during pregnancy leads to decreased lung function in offspring. However, the effects of maternal CS exposure on lung autophagy and ER stress in offspring during pregnancy remain unclear. METHODS: C57BL/6J female mice were divided into the AA (air treatment during both pre-pregnancy and pregnancy), AS (air treatment during pre-pregnancy and CS treatment during pregnancy), SA (CS treatment during pre-pregnancy and air treatment during pregnancy), and SS (CS treatment during both pre-pregnancy and pregnancy) groups. The male offspring mice were selected to the study and euthanized 49 days after birth for the study. Hematoxylin and eosin (HE) staining was employed to observe pathological alterations, while transmission electron microscopy (TEM) was utilized to examine ultrastructure and autophagic vesicles. Additionally, the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) method was applied to identify apoptosis in lung tissues. Immunofluorescence, Real-Time PCR, and Western Blot analyses were conducted to assess the expression of ER stress and autophagy-related markers in lung tissues. RESULTS: The findings revealed that exposure to CS heightened the extent of pathological damage and the abundance of autophagosomes in the lungs of offspring mice. TUNEL results indicated an increased fluorescence intensity in the AS, SA and SS groups, with the most significant in AS and SS groups. Furthermore, CS exposure augmented the fluorescence intensity and expression of ER stress and autophagy-related proteins. The expression of C/EBP-homologous protein (CHOP) exhibited no discernible difference between the SA and SS groups but showed a significant increase in the AS group. Conversely, the expression levels of glucose-regulated protein 78 (GRP78), Caspase-12, Beclin-1, and microtubule-associated protein 1 light chain 3 (LC3) exhibited no significant difference between the AS and SA groups, whereas they were significantly upregulated in the SS group. CONCLUSIONS: Preconceptional and gestational exposure to CS heightened ER stress and autophagy in the lungs of mouse offspring. However, in mothers who smoked, withdrawal from CS during pregnancy led to a reduction in ER stress and autophagy in the lungs of their offspring.

ECAS correlation with metabolic alterations on FDG-PET imaging in ALS.

Background: Cognitive impairment is observed in up to 50% of patients with amyotrophic lateral sclerosis (ALS). The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is an ALS-specific multi-domain screening tool. Few studies have examined the relationship between ECAS scores and [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) findings. Objective: To assess the relationship between ECAS scores and glucose metabolism patterns on [18F]FDG -PET images in ALS. Methods: We collected [18F]FDG-PET images from 65 patients with ALS and 39 healthy controls. ECAS scores were collected on all patients and we calculated the correlation to [18F]FDG-PET in order to investigate the potential links between cognition and glucose metabolism. Results: We observed hypometabolism in the frontal cortex, insula, and limbic system, together with hypermetabolism in the cerebellum in patients with ALS compared to controls. A lower ECAS total score was associated with lower glucose metabolism in the right orbitofrontal gyrus and higher glucose metabolism in lateral occipital, medial occipital, and cerebellar regions, among patients with ALS. Similar results, although less widespread, were observed in the analyses of ECAS ALS-specific scores. Conclusions: The metabolic patterns in [18F]FDG -PET show that changes in the glucose metabolism of corresponding areas are related to cognitive dysfunction in ALS, and can be detected using the ECAS.

Corrosion Behavior of 20G and TP347H in Molten LiCl-NaCl-KCl Salt.

The corrosion behavior of 20G and TP347H materials was investigated in molten LiCl-NaCl-KCl salt. The corrosion rates of these materials in molten chloride salt are high and are strongly affected by the alloying surface oxide formation. The 20G shows uniform surface corrosion with almost no protective oxide formation on the surface. In contrast, the austenitic steel TP347H exhibits better corrosion resistance in molten chloride salts due to its high Cr content. Owing to the highly corrosive nature of molten chloride salts, the Cl- in molten salt could react with oxides and alloy, inducing intergranular corrosion of austenitic steel in molten chloride salt environments.

Light-Sheet Microscopic Imaging of Whole-Mouse Vascular Network with Fluorescent Microsphere Perfusion.

Visualizing the whole vascular network system is crucial for understanding the pathogenesis of specific diseases and devising targeted therapeutic interventions. Although the combination of light sheet microscopy and tissue-clearing methods has emerged as a promising approach for investigating the blood vascular network, leveraging the spatial resolution down to the capillary level and the ability to image centimeter-scale samples remains difficult. Especially, as the resolution improves, the issue of photobleaching outside the field of view poses a challenge to image the whole vascular network of adult mice at capillary resolution. Here, we devise a fluorescent microsphere vascular perfusion method to enable labeling of the whole vascular network in adult mice, which overcomes the photobleaching limit during the imaging of large samples. Moreover, by combining the utilization of a large-scale light-sheet microscope and tissue clearing protocols for whole-mouse samples, we achieve the capillary-level imaging resolution (3.2 × 3.2 × 6.5 µm) of the whole vascular network with dimensions of 45 × 15 × 82 mm in adult mice. This method thus holds great potential to deliver mesoscopic resolution images of various tissue organs for whole-animal imaging.

Sustainable biochar application in anammox process: Unveiling novel pathways for enhanced nitrogen removal and efficient start-up at low temperature.

This study explored the use of biochar to accelerate the establishment of anaerobic ammonium oxidation (anammox) reactors operating at 15 ± 1℃. Incorporating 10 g/L bamboo charcoal in S1 accelerated the start-up of anammox in 87 days, which was significantly shorter than 103 days in S0 (without biochar). After 140 days, S1 exhibited a 10.9 % increase in nitrogen removal efficiency due to a 28.9 % elevation in extracellular polymeric substances, bolstering anammox bacterial resilience. Predominant anammox bacteria (Cadidatus Brocadia and Cadidatus Jettenia) showed relative abundances of 3.19 % and 0.38 % in S1, respectively, which were significantly higher than 0.40 % and 0.05 % in S0. Biochar provides favorable habitats for the enrichment of anammox bacteria and accelerates the establishment of anammox at low temperatures. This finding holds promise for enhancing the efficiency of anammox in cold climates and advancing sustainable wastewater nitrogen removal.

Airway hillocks are injury-resistant reservoirs of unique plastic stem cells.

Airway hillocks are stratified epithelial structures of unknown function1. Hillocks persist for months and have a unique population of basal stem cells that express genes associated with barrier function and cell adhesion. Hillock basal stem cells continually replenish overlying squamous barrier cells. They exhibit dramatically higher turnover than the abundant, largely quiescent classic pseudostratified airway epithelium. Hillocks resist a remarkably broad spectrum of injuries, including toxins, infection, acid and physical injury because hillock squamous cells shield underlying hillock basal stem cells from injury. Hillock basal stem cells are capable of massive clonal expansion that is sufficient to resurface denuded airway, and eventually regenerate normal airway epithelium with each of its six component cell types. Hillock basal stem cells preferentially stratify and keratinize in the setting of retinoic acid signalling inhibition, a known cause of squamous metaplasia2,3. Here we show that mouse hillock expansion is the cause of vitamin A deficiency-induced squamous metaplasia. Finally, we identify human hillocks whose basal stem cells generate functional squamous barrier structures in culture. The existence of hillocks reframes our understanding of airway epithelial regeneration. Furthermore, we show that hillocks are one origin of 'squamous metaplasia', which is long thought to be a precursor of lung cancer.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Increased FGF-21 Improves Ectopic Lipid Deposition in the Liver and Skeletal Muscle.

Obesity can lead to excessive lipid accumulation in non-adipose tissues, such as the liver and skeletal muscles, leading to ectopic lipid deposition and damaging target organ function through lipotoxicity. FGF-21 is a key factor in regulating lipid metabolism, so we aim to explore whether FGF-21 is involved in improving ectopic lipid deposition. We observed the characteristics of ectopic lipid deposition in the liver and skeletal muscles of obesity-resistant mice, detected the expression of FGF-21 and perilipin, and found that obesity-resistant mice showed a decrease in ectopic lipid deposition in the liver and skeletal muscles and increased expression of FGF-21. After inhibiting the expression of FGF-21, a more severe lipid deposition in liver cells and skeletal muscle cells was found. The results indicate that inhibiting FGF-21 can exacerbate ectopic lipid deposition via regulating lipid droplet synthesis and decomposition, as well as free fatty acid translocation and oxidation. In conclusion, FGF-21 is involved in improving ectopic lipid deposition caused by obesity in the liver and skeletal muscles.

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions.

OBJECTIVE: To evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree's outcomes. MATERIALS AND METHODS: Imaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model. RESULTS: Size, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05). CONCLUSION: The diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

High Manganese Content of Lipid NanoMn (LNM) by Microfluidic Technology for Enhancing Anti-Tumor Immunity.

Immunotherapy is a clinically effective method for treating tumors. Manganese can activate the cGAS-STING signaling pathway and induce an anti-tumor immune response. However, its efficacy is hindered by non-specific distribution and low uptake rates. In this study, we employed microfluidic technology to design and develop an innovative preparation process, resulting in the creation of a novel manganese lipid nanoparticle (LNM). The lipid manganese nanoparticle produced in this process boasts a high manganese payload, excellent stability, the capacity for large-scale production, and high batch repeatability. LNM has effectively demonstrated the ability to activate the cGAS-STING signaling pathway, induce the production of pro-inflammatory cytokines, and inhibit tumor development. Notably, LNM does not require combination chemotherapy drugs or other immune activators. Therefore, LNM presents a safe, straightforward, and efficient strategy for anti-tumor immune activation, with the potential for scalable production.